Traditional Simulation Research Articles

Cardiac Crisis Simulations: Traditional vs. Escape Room Learning for Nursing Students

Background To meet the diverse learning needs of nursing students, educators must develop teaching strategies that increase confidence and satisfaction with learning, as well as promote critical thinking and clinical judgment. Aim This study aimed to compare the impact of Traditional Simulation (TS) learning versus Escape Room Simulation (ERS) learning on nursing students’ satisfaction and self-confidence in caring for a patient experiencing an acute cardiac event. It was hypothesized that the escape room simulation would provide similar or improved outcomes in terms of satisfaction and self-confidence as compared to traditional simulation. Methods In this experimental design study, students’ self-reported responses to the Student Satisfaction and Self-Confidence in Learning (SSSCL) survey were compared between the simulation (n = 20) and escape room (n = 18) cohorts in a pre-licensure baccalaureate nursing program. Results There was no significant difference in satisfaction with current learning and self-confidence in learning between the simulation groups and the escape room groups. Conclusion The findings suggest that escape room simulation-based education can be used in lieu of traditional simulation without compromising students’ confidence and satisfaction with learning.

Localized Heating Assisted Laser Shock Peening without Coating Enhances Mechanical Properties of Ti6Al4V Alloys

Room-temperature laser shock peening without coating (RT-LSPwoC) is the mainstream method for surface strengthening, yet it induces tensile residual stress on the surface during rapid heating and cooling. Warm LSPwoC, combining the benefits of dynamic strain aging and LSPwoC, exhibits higher performance than RT-LSPwoC. However, overall high-temperature is time- and resource-consuming, and it is prone to causing thermal stress relaxation during processing, making it unsuitable for large-scale applications. In this paper, a novel localized heating assisted LSPwoC (LHA-LSPwoC) method, utilizing a continuous wave laser for local heating, is proposed. Additionally, the deep physics-informed neural network model, embedded with physical formulas, is designed for process optimization. It enables rapid and accurate responses for an extensive number of cases (40 cases with an average deviation below 1%), overcoming the drawbacks of traditional numerical simulations that are time-consuming and difficult to efficiently handle numerous cases. Compared to RT-LSPwoC, LHA-LSPwoC samples have higher dislocation density and higher grain refinement, demonstrating higher hardness and residual stress, particularly superior fatigue performance. The mechanism of LHA-LSPwoC is discussed, and thermally coupled finite element simulations and molecular dynamics calculations are conducted to provide theoretical support. The proposed method is cost-effective and flexible, showing outstanding potential for industrial applications.

Improving Nanoparticle Size Estimation from Scanning Transmission Electron Micrographs with a Multislice Surrogate Model.

The computational cost of simulating scanning transmission electron microscopy (STEM) images limits the curation of large enough data sets to train accurate and robust machine learning networks for deep feature extraction from atomically resolved STEM images. For nanoparticle size estimation in particular, a diverse data set is essential due to the large variations in size, shape, crystallinity, orientation, and dynamical diffraction effects in experimental data. To address this, we train a 3D convolutional neural network to predict STEM images from voxelized atomic models, achieving a 100x speed-up compared to traditional multislice simulations while maintaining high image quality. We then generate a data set of 100.000 synthetic multislice images and investigate the performance of different size-estimator architectures as a function of training set size. A ResNet18-based model trained on 4000 real and 100.000 synthetic images is found to perform the best, reducing the median size-estimation error from 9.89% without synthetic data to 5.26%.

Revolutionising Osseous Biopsy: The Impact of Artificial Intelligence in the Era of Personalised Medicine.

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalised medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalised medicine. This paper explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalised medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation and generative models. We explore their roles in tumour board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalisability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.

Calculations of extreme sea level rise scenarios are strongly dependent on ice sheet model resolution

The West Antarctic Ice Sheet (WAIS) is losing ice and its annual contribution to sea level is increasing. The future behaviour of WAIS will impact societies worldwide, yet deep uncertainty remains in the expected rate of ice loss. High-impact low-likelihood scenarios of sea-level rise are needed by risk-averse stakeholders but are particularly difficult to constrain. Here, we combine traditional model simulations of the Amundsen Sea sector of WAIS with Gaussian process emulation to show that ice-sheet models capable of resolving kilometre-scale basal topography will be needed to assess the probability of extreme scenarios of sea-level rise. This resolution exceeds many state-of-the-art continent-scale simulations. Our ice-sheet model simulations show that coarser resolutions tend to project a larger range of sea-level contributions than finer resolutions, inflating the tails of the distribution. We therefore caution against relying purely upon simulations 5 km or coarser when assessing the potential for societally important high-impact sea-level rise.

Investigation of Spatio-Temporal Simulation of Mining Subsidence and Its Determinants Utilizing the RF-CA Model

It is important to carry out timely scientific assessments of surface subsidence in coal resource cities for ecological environmental protection. Traditional subsidence simulation methods cannot quantitatively describe the driving factors that contribute to or ignore the dynamic connections of subsidence across time and space. Thus, a novel spatio-temporal subsidence simulation model is proposed that couples random forest (RF) and cellular automaton (CA) models, which are used to quantify the contributions of driving factors and simulate the spatio-temporal dynamic changes in subsidence. The RF algorithm is first utilized to clarify the contributions of the driving factors to subsidence and to formulate transformation rules for simulation. Then, a spatio-temporal simulation of subsidence is accomplished by combining it with the CA model. Finally, the method is validated based on the Yongcheng coalfield. The results show that the depth–thickness ratio (0.242), distance to the working face (0.159), distance to buildings (0.150), and lithology (0.147) play main roles in the development of subsidence. Meanwhile, the model can effectively simulate the spatio-temporal changes in mining subsidence. The simulation results were evaluated using 2021 subsidence data as the basis data; the simulation’s overall accuracy (OA) was 0.83, and the Kappa coefficient (KC) was 0.71. This method can obtain a more realistic representation of the spatio-temporal distribution of subsidence while considering the driving factors, which provides technological support for land-use planning and ecological and environmental protection in coal resource cities.

Bidirectional Pattern Recognition and Prediction of Bending-Active Thin Sheets via Artificial Neural Networks

Currently, active-bending structures and their shape optimization techniques have become a hot topic in the design of spatial structures and freeform buildings. However, their form-finding process is usually time-consuming, and the application of finite element methods (FEM) requires huge computational effort. In the face of these challenges, artificial intelligence techniques have great potential for application and bring many important advantages to this field. In this paper, we propose a novel, data-driven, bidirectional prediction method based on artificial neural networks. It can both forward infer the bending deformation shapes of a thin plate under specific complex conditions and reverse infer the boundary conditions necessary for a given bending shape. In comparison to traditional active-bending simulation, the proposed method is quicker and simpler to utilize during the design process and facilitates reverse predictions. Communication between design and construction can be facilitated to ensure quality and efficiency in the construction of relevant bent structural components. It is experimentally demonstrated that the network can control the mean value of prediction deviation below 40 mm for a 4 m × 0.5 m aluminum plate.

Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning

The prediction of blast loading for complex structures using deep learning requires extensive training data from field experiments or numerical simulations. However, the destructive nature of explosions complicates the collection of adequate field data, and traditional simulations are often time-consuming. To address these challenges, a Bayesian deep learning approach is proposed that quantifies prediction uncertainty. This method utilizes an uncertain selection strategy to actively choose high-quality samples, enhancing the simulation process and iteratively expanding the training dataset. The experimental results demonstrate that this Bayesian deep active learning method achieves a mean absolute percentage error (MAPE) of 6.1% for peak overpressure predictions. Additionally, more than 73.1% of confidence intervals include true values, with prediction times under 20 ms for single-point blasts. Notably, only 60% of the training data is required to achieve the same accuracy as conventional deep learning methods. This approach facilitates rapid and reliable predictions of blast loading for complex structures while significantly reducing training costs.

Advanced Mechanical Analysis Workflow for Chiplets Integration Predictive Design

Chiplet-based designs offer a compelling value proposition compared to traditional monolithic System on Chip (SoC) architectures with advantages in IP reuse, performance, cost, and time to market. Mechanical modeling and analysis (M&A) play a critical role in enabling the integration of chiplets, spanning the chip-package-board-system domains to support product development. The integration of chiplets from various suppliers and deverse technologies introduces differences in material properties, thermal coefficients, and mechanical behaviors that presents a significant challenge in ensuring a good mechanical reliability across the chiplet-based integration system. Consequently, chiplet-based designs encounter more challenges to address the elevated mechanical stress and reliability concerns. Traditional mechanical analysis and simulation workflows are primarily tailored for monolithic SoC-based designs and are not readily applicable to chiplet-based designs. The latter can no longer afford to overlook issues related to mechanical and thermal stress induced by multiple chiplets, especially with the use of advanced 2.5D and high-density fanout packages. In this paper, an advanced mechanical analysis workflow for chiplet-based designs is proposed. The representative examples that demonstrate the efficacy of this workflow are also presented. The advanced mechanical analysis workflow involves modeling of chiplets interactions at the early design stage spanning from the Si level to package level and encompassing scales from micrometers to millimeters that includes the components such as Through-Silicon Vias (TSVs), HIgh Density Fanout Cu RDL (Redistribution Layer) and microbumps. Furthermore, the predictive mechanical analysis workflow extends to analyses at the millimeter to meter scale, considering entire systems and incorporating elements like heat sinks and printed circuit boards (PCBs). It represents a multifaceted process spanning chip-to-package co-design, stress analysis, thermal analysis, failure analysis, and chiplet-based design optimization.

New perspectives on facial asymmetries thanks to 3D vision

Modern orthodontics is undergoing a revolution with the advent of 3D imaging, offering unprecedented perspectives for the evaluation and treatment of facial asymmetries. These asymmetries, whether mandibular, maxillary, or dental, require a deeper understanding of their causes and their aesthetic and functional impact. Additionally, associated functional imbalances must be addressed for comprehensive management. Traditional diagnostic methods, such as 2D radiographs and plaster models, have significant limitations, now overcome by the use of 3D technologies (intraoral scanners, Cone Beam Computed Tomography, photogrammetry). This article aims to demonstrate how these technologies enhance the understanding and precise management of craniofacial anomalies. The clinical case presented involves a 28-year-old female. The results obtained using 3D imaging are compared with traditional methods and initial simulations, highlighting their advantages. 3D imaging opens promising perspectives in orthodontics, particularly in personalized treatments and innovative approaches such as mandibular kinetics (transition to 4D). Moreover, the increasing integration of artificial intelligence into diagnostic and treatment planning tools adds further value, streamlining decision-making and improving clinical efficiency. 3D imaging represents a turning point in the evaluation and treatment of facial asymmetries. By providing a clearer and more detailed view of anatomical structures, it allows orthodontists to design more precise and personalized treatment plans. These advancements lead to optimized clinical outcomes and improved patient compliance.

Pipa Performance Analysis and Timbre Simulation Based on Artificial Intelligence

As a traditional Chinese musical instrument, pipa is famous for its unique timbre and complex and varied playing techniques. It is an important symbol of Chinese music culture. However, its complexity also makes its digital protection and research face many challenges. There are many kinds of playing techniques for pipa, such as finger rolling, plucking, bending, sweeping and overtones, each technique gives it a unique timbre change. In addition, the timbre of the pipa presents a variety of expressions due to the playing force, speed and the performer's skills, which puts higher demands on traditional music research and simulation technology.With the rapid development of artificial intelligence technology, especially breakthroughs in audio signal processing and deep learning, new solutions have been provided for the research of pipa art. By combining machine learning models and generative adversarial networks (GAN), significant progress can be made in technique analysis and timbre simulation. This study aims to use artificial intelligence technology to analyze the performance characteristics of the pipa and generate realistic timbre simulations, providing technical support for the digital protection and dissemination of traditional music.

Effectiveness of a blended (distance and in-person) simulation education program on advanced cardiac life support for nursing students using the PARTNER model

BackgroundThe incidence of acute cardiac arrest is increasing and is directly linked to patient survival, highlighting the critical role of nurses. Advanced nursing skills for cardiac arrest patients, such as self-directed pre-learning applied to various clinical situations, require a systematic blended learning approach to integrate knowledge and enhance clinical performance through face-to-face practice. The purpose of this study is to evaluate the effectiveness of a blended simulation education program for Advanced Cardiac Life Support (ACLS) using the PARTNER model.MethodsA nonequivalent control group pretest-posttest design with a time-lagged approach was used to collect data from 34 third-year nursing students. The experimental group(n = 17) received a 480-minute blended simulation education program based on the PARTNER model and flipped learning, while the control group(n = 17) participated in a 480-minute traditional simulation education program.ResultsThe study found that knowledge, confidence, performance in advanced cardiac life support, and clinical performance ability were effectively enhanced, although no significant effect was observed in learning satisfaction.ConclusionsThe program provides a new direction in applying blended simulation using the PARTNER model for advanced cardiac life support nursing education, ultimately contributing to the training of nurses competent in advanced cardiac life support.

Impactor shape effect on the shock mitigation behavior of metamaterial structures

Auxetic metamaterial structures, characterized by their negative Poisson’s ratio, have shown promising mechanical performance, particularly in shock impact scenarios. However, the complex and scenario-dependent behavior of these structures necessitates comprehensive and appropriate evaluation and verification. In this study, the shock mitigation property of an auxetic planar design in relation to the shape of the impactor surface was discussed using numerical simulation results, validated using drop testing shock experiments. Results revealed that both metamaterial shape and impactor geometry significantly influenced behavior of metamaterial response. For the plate impactor, minimal deformation at lower velocities resulted in higher peak accelerations for metamaterial configurations. Conversely for the cylindrical impactor, metamaterial structures consistently enhanced shock mitigation across all impact velocities. An Artificial Neural Network (ANN) model was developed to predict the vertical direction acceleration of the impactor by also incorporating the shape of the impactor as an evaluation parameter, in addition to the metamaterial dimensions and impact velocity. Validation with impact scenarios outside the subset of the training dataset confirmed the ANN model’s accuracy, achieving at least 94% accuracy for both impactor cases, thereby offering an efficient alternative to traditional experimental and numerical simulations for studying metamaterial shock mitigation behavior.

Monitoring and Characterizing GPU Usage

ABSTRACTFor systems with an accelerator component, it is important from an operational and planning perspective to understand how and to what extent the accelerators are being used. Having a framework for tracking the utilization of accelerator resources is important both for judging how efficiently used a system is and for capacity and configuration planning of future systems. In addition to tracking total utilization and accelerator efficiency numbers, some attention should also be paid to the types of research and workflows that are being executed on the system. In the past, the demand for accelerator resources was largely driven by more traditional simulation codes, such as molecular dynamics. But with the growing popularity of deep learning and artificial intelligence workflows, accelerators have become even more highly sought after and are being used in new ways. Provisioning resources to researchers via an allocation system allows sites to track a project's usage and workflow as well as the scientific impact of the project. With such tools and data in hand, characterizing the GPU utilization of deep learning frameworks versus more traditional GPU‐enabled applications becomes possible. In this paper we present a survey of GPU monitoring tools used in sites and a framework for tracking the utilization of NVIDIA GPUs on Slurm‐scheduled HPC systems used at Indiana University. We also present an analysis of accelerator utilization on multiple systems, including an HPE Apollo system targeting AI workflows and a Cray EX system.

Machine learning-based laser heterodyne photothermal displacement method: simultaneous estimation of silicon thermal diffusivity and carrier lifetime

Abstract The laser heterodyne photothermal displacement (LH-PD) method, a recently developed photothermal technique, enables measurement of absolute surface displacements, which are otherwise challenging to measure with other photothermal methods. This method offers significant potential for quantifying physical properties that are difficult to achieve with traditional photothermal methods. In this study, we aimed to estimate the thermal diffusivity and carrier lifetime of Si using a machine learning model based on the time variation of the displacement obtained using the LH-PD method. By leveraging the machine learning model, we generated predictive mappings of thermal diffusivity and carrier lifetime of a pattern-etched Si wafer from the displacement mappings. Furthermore, our findings demonstrated that fine-tuning the model enabled accurate predictions of the carrier lifetime. While traditional simulations require tens of hours to estimate the material parameters, machine learning reduced this process to only a few seconds.

Transistor Frequency-Response Analysis: Recursive Shunt-Circuit Transformations

Frequency-response analysis is critical in circuit design. Frequency response encodes crucial information, like gain, accuracy, bandwidth, response time, phase shift, stability, and more. Unfortunately, existing methods are either algebraic and obscure or approximations with inaccuracies. So applying them to more complex circuits is often arduous or unreliable. This paper proposes recursive shunt-circuit transformations: a simple, rigorous, and insightful analytical method for conceptualizing and designing electronic circuits. The method asserts that (a) each equivalent capacitance shunts away its parallel resistance past its RC frequency. This (b) decreases the gain (induces a pole) and (c) changes the circuit. (d) The next dominant capacitance shunts its parallel resistance past the next pole and so on until all remaining capacitances shunt their parallel resistances past the poles they establish. The method also asserts that (e) bypass capacitances increase gain (induce zeros) and (f) cross-amp capacitances couple stages and poles. By applying this method and concepts, designers can (i) simplify an arbitrarily complex circuit into simpler coupled/decoupled stages and (ii) determine and manage poles and zeros with insight. This method was applied to design and analyze single- and multi- stage amplifier circuits and results were benchmarked against traditional methods and NGSPICE simulations, demonstrating its accuracy and broad applicability.

Artificial Neural Networks as a Tool for High-Accuracy Prediction of In-Cylinder Pressure and Equivalent Flame Radius in Hydrogen-Fueled Internal Combustion Engines

The automotive industry is under increasing pressure to develop cleaner and more efficient technologies in response to stringent emission regulations. Hydrogen-powered internal combustion engines represent a promising alternative, offering the potential to reduce carbon-based emissions while improving efficiency. However, the accurate estimation of in-cylinder pressure is crucial for optimizing the performance and emissions of these engines. While traditional simulation tools such as GT-POWER are widely utilized for these purposes, recent advancements in artificial intelligence provide new opportunities for achieving faster and more accurate predictions. This study presents a comparative evaluation of the predictive capabilities of GT-POWER and an artificial neural network model in estimating in-cylinder pressure, with a particular focus on improvements in computational efficiency. Additionally, the artificial neural network is employed to predict the equivalent flame radius, thereby obviating the need for repeated tests using dedicated high-speed cameras in optical access research engines, due to the resource-intensive nature of data acquisition and post-processing. Experiments were conducted on a single-cylinder research engine operating at low-speed and low-load conditions, across three distinct relative air–fuel ratio values with a range of ignition timing settings applied for each air excess coefficient. The findings demonstrate that the artificial neural network model surpasses GT-POWER in predicting in-cylinder pressure with higher accuracy, achieving an RMSE consistently below 0.44% across various conditions. In comparison, GT-POWER exhibits an RMSE ranging from 0.92% to 1.57%. Additionally, the neural network effectively estimates the equivalent flame radius, maintaining an RMSE of less than 3%, ranging from 2.21% to 2.90%. This underscores the potential of artificial neural network-based approaches to not only significantly reduce computational time but also enhance predictive precision. Furthermore, this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis, offering a practical advantage in real-time diagnostics.

Improving Resident Physician History and Physical in Challenging Situations.

To educate physician trainees using simulation on best management of children with autism spectrum disorder who have neurocognitive and behavioral challenges when experiencing acute illness. A simulation-based curriculum including baseline assessment, communication techniques, and use of calming resources was developed to educate residents in assessing children with sensory barriers. Traditional simulation and deliberate practice were used to teach this curriculum to second- and third-year pediatric and internal medicine-pediatric residents. Using alternating assignment, residents were placed into 3 groups: deliberate practice, traditional simulation-based debriefing, and control (no simulation training). The residents were assessed on the initial visit, 2 weeks and 4 to 6 months follow-up periods with a sensory assessment tool. Assessment scores were analyzed using a general linear mixed model with random intercept based on Poisson regression to approximate distribution of the total score. Forty-three residents participated: 46.5% men, 53.5% women. There were 15, 16, and 12 residents in the control, deliberate practice and traditional simulation groups, respectively. Both teaching styles showed improved performance at 2 weeks follow-up. In addition, these groups demonstrated higher performance when compared with the control group. We were able to show that resident physicians were able to learn important principles and retain them during 4 to 6 months follow-up. However, there was not a difference between 2 teaching styles both in improvement and retention. Simulation education, both deliberate and traditional, can be used to enhance teaching of key components in assessing children with sensory and communication barriers, including those with autism spectrum disorder when presenting with acute illness.

Neural network surrogate and projected gradient descent for fast and reliable finite element model calibration: A case study on an intervertebral disc.

Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability and use in diagnosing and planning treatments. However, traditional calibration methods are computationally intensive, requiring iterative, derivative-free optimization algorithms that often take days to converge. This study addresses these challenges by introducing a novel, efficient, and effective calibration method demonstrated on a human L4-L5 IVD FE model as a case study using a neural network (NN) surrogate. The NN surrogate predicts simulation outcomes with high accuracy, outperforming other machine learning models, and significantly reduces the computational cost associated with traditional FE simulations. Next, a Projected Gradient Descent (PGD) approach guided by gradients of the NN surrogate is proposed to efficiently calibrate FE models. Our method explicitly enforces feasibility with a projection step, thus maintaining material bounds throughout the optimization process. The proposed method is evaluated against state-of-the-art Genetic Algorithm (GA) and inverse model baselines on synthetic and in vitro experimental datasets. Our approach demonstrates superior performance on synthetic data, achieving a Mean Absolute Error (MAE) of 0.06 compared to the baselines' MAE of 0.18 and 0.54, respectively. On experimental specimens, our method outperforms the baseline in 5 out of 6 cases. While our approach requires initial dataset generation and surrogate training, these steps are performed only once, and the actual calibration takes under three seconds. In contrast, traditional calibration time scales linearly with the number of specimens, taking up to 8 days in the worst-case. Such efficiency paves the way for applying more complex FE models, potentially extending beyond IVDs, and enabling accurate patient-specific simulations.

SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network.

The SiC MOSFET with an integrated SBD (SBD-MOSFET) exhibits excellent performance in power electronics. However, the static and dynamic characteristics of this device are influenced by a multitude of parameters, and traditional TCAD simulation methods are often characterized by their complexity. Due to the increasing research on neural networks in recent years, such as the application of neural networks to the prediction of GaN JBS and Finfet devices, this paper considers the application of neural networks to the performance prediction of SiC MOSFET devices with an integrated SBD. This study introduces a novel approach utilizing neural network machine learning to predict the static and dynamic characteristics of the SBD-MOSFET. In this research, SBD-MOSFET devices are modeled and simulated using Sentaurus TCAD(2017) software, resulting in the generation of 625 sets of device structure and sample data, which serve as the sample set for the neural network. These input variables are then fed into the neural network for prediction. The findings indicate that the mean square error (MSE) values for the threshold voltage (Vth), breakdown voltage (BV), specific on-resistance (Ron), and total switching power dissipation (E) are 0.0051, 0.0031, 0.0065, and 0.0220, respectively, demonstrating a high degree of accuracy in the predicted values. Meanwhile, in the comparison of convolutional neural networks and machine learning, the CNN accuracy is much higher than the machine learning methods. This method of predicting device performance via neural networks offers a rapid means of designing SBD-MOSFETs with specified performance targets, thereby presenting significant advantages in accelerating research on SBD-MOSFET performance prediction.